Welcome to the Parallaxis Blog
Explore our latest thoughts on all things related to AI, Machine Learning, and the importance of data .
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5 Key Indicators That AI Isn't The Answer
This post outlines five key considerations for deciding whether to pursue AI: the existence of clear rules, data volume and variability, significant regulatory requirements, static environments, and unclear ROI. AI should be a multiplier for disciplined thinking and well-designed processes, not a replacement. Focusing on data quality and automation maturity should precede any AI adoption.
Compliance, Coffee, and Machine Learning
Large Language Models (LLMs) can transform compliance document review from a slow, error-prone and manual process into a scalable, efficient operation. Unlike traditional automation that relied on rigid keyword matching, LLMs understand context and nuance, and when trained on organization-specific examples—such as approved contracts and compliance templates—they can assess documents against internal governance standards.
The Illusion of Just Knowing
Introducing AI/ML in your business emphasizes the importance of data for understanding business operations and driving growth. Relying on assumptions hinders progress and creates an illusion of competence. The document advocates metrics, experimentation, and the scientific method to create a data-driven approach to doing business.
There is no Bad Data
Data's value depends on its intended use. Operational data collection often prioritizes transactions over analysis, resulting in data not optimized for later purposes. Technical data aggregation can introduce biases. Unclear business requests and data silos complicate analysis. To leverage data effectively, we need to be flexible on how we analyze the data we have at hand.
Practical Business Reasons to Resist the Allure of AI
There are many traps along the journey required to leverage AI/ML to generate value for your business. Success relies on aligning AI/ML initiatives with clear business objectives and understanding their true potential.
Anti-Patterns in Data Mesh
This article explores common anti-patterns in implementing Data Mesh, a decentralized data architecture emphasizing domain-oriented data ownership. While Data Mesh aims to enhance data accessibility and usability across organizations, its success relies on understanding core principles: domain-driven data ownership, data products, and federated governance.
Data Mess to Data Mesh
The standard strategy of centralizing data into a single repository often leads to chaotic "data swamps.” Due to poor data quality and governance issues, these swaps hinder efficient analysis and decision-making. An alternative approach, known as Data Mesh, proposes a decentralized architecture focused on treating data as a product.
Question Formation and Data Analysis in Data Science
This blog post focuses on the first phase of our Data Science Process: Question Formation and Data Analysis. In this phase, we iterate multiple times through question formation, data collection, and exploration. Initial questions are likely to be of low fidelity. Through the process of data exploration, the questions gain fidelity and drive toward business value.
Introducing a Data Science Process for AI/ML
This is an introduction to a series of blog posts describing the process of creating and operating data models in support of your AI/Machine Learning (ML) programs. It is structured to ensure that you can deliver actual business value.
Your Starter Guide to Data Governance
Data governance establishes standards for data collection, storage, and analysis, ensuring accuracy and mitigating risks associated with regulatory non-compliance. Moreover, governance promotes ethical data practices, safeguarding individual privacy rights and societal norms.